Stochastic Optimal Energy Management of Smart Home with PEV Energy Storage
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Stochastic Optimal Energy Management of Smart Home with PEV Energy Storage Xiaohua Wu, Xiaosong Hu, Member, IEEE, Xiaofeng Yin, Member, IEEE, Scott J. Moura, Member, IEEE Abstract—This paper proposes a stochastic dynamic program- Rint The internal resistance ming framework for the optimal energy management of a smart S The PEV state home with plug-in electric vehicle (PEV) energy storage. This SOC The battery state-of-charge work is motivated by the challenges associated with intermit- max tent renewable energy supplies and the local energy storage SOC The maximal SOC min opportunity presented by vehicle electrification. This paper seeks SOC The minimal SOC min to minimize electricity ratepayer cost, while satisfying home SOCc The constant minimal SOC power demand and PEV charging requirements. First, various SOCg The sample values from the discretized set of feasible operating modes are defined, including vehicle-to-grid (V2G), SOC values vehicle-to-home (V2H), and grid-to-vehicle (G2V). Second, we use equivalent circuit PEV battery models and probabilistic SOCh The sample values from the discretized set of feasible models of trip time and trip length to formulate the PEV to SOC values smart home energy management stochastic optimization problem. SOCinit The initial SOC Finally, based on time-varying electricity price and time-varying SOCpi The SOC at plugging-in time home power demand, we examine the performance of the three SOC The SOC at plugging-out time operating modes for typical weekdays. po Index Terms—Vehicle to Grid, Energy Management, Stochastic ta The plugging-in time Dynamic Optimization, Smart Home, Plug-in Electric Vehicle. td The plugging-out time Voc The open circuit voltage NOMENCLATURE I. INTRODUCTION ∆t The time-step A. Motivation and Background c The time-varying electricity price d The trip distance PEV energy storage provides a compelling opportunity to address several challenges for the security and economic sus- Eff The overall electric drive efficiency I Current tainability of our energy supply [1]. These challenges include Imax The maximal current renewable energy integration, distributed energy resources, Imin The minimal current resilience during natural/man-made disasters (e.g. the tsunami- min induced Fukushima nuclear meltdown), and rapidly evolving Ibat The physical limits of battery discharging current k Time index markets for demand-side management. If left unmanaged, however, PEVs can exacerbate peak loads and overstress local mhg The probability that pluggging-in SOC SOCpi = distribution circuits, resulting in less stable electricity supply SOCh, given plugging-out SOC SOCpo = SOCg p The transition probability of plugging-out and higher costs ultimately passed on to the consumer. As a consequence, researchers have recently focused on developing Pbatt The PEV battery power effective control strategies for integrating PEVs into building Pdem The power demand of the house loads and the grid. Pgrid The electric power from the grid q The transition probability of plugging-in B. Literature Review Q The charge capacity cap Researchers have examined PEV charging schedule designs Q The energy capacity eap for objectives such as ancillary services, frequency regulation, Corresponding authors. X. Wu and X. Hu equally contributed to this work. battery health, and effective utilization of renewable energy. X. Wu is with School of Automobile and Transportation, Xihua University, The application of aggregators to frequency regulation by Chengdu, 610039, China. She was with Energy, Controls, and Applications making fair use of PEVs’ energy storage capacity is addressed Laboratory, University of California, Berkeley, CA 94720, USA. (e-mail: [email protected]). in [2], [3]. Vehicle to Grid (V2G) ancillary services, such X. Hu is with the State Key Laboratory of Mechanical Transmissions as load regulation and spinning reserves are studied in [4] and with the Department of Automotive Engineering, Chongqing University, by incorporating probabilistic vehicle travel models, time Chongqing 400044, China. He was with Energy, Controls, and Applications Laboratory, University of California, Berkeley 94720, California, USA. (E- series pricing, and reliability. The cost of PEV battery wear mail: [email protected]). due to V2G applications is presented in [5], [6]. Renewable X. Yin is with School of Automobile and Transportation, Xihua University, integration is considered in [7], which derives optimal PEV Chengdu, 610039, China. (e-mail: [email protected] ). S. J. Moura is with Energy, Controls, and Applications Laboratory, Univer- charging schedules based on predicted photovoltaic output and sity of California, Berkeley, CA 94720, USA. (e-mail: [email protected]). electricity consumption. The literature provides various V2G energy management approaches [8], [9], which can be generally categorized into linear programming (LP) [10], [11], dynamic programming (DP) [12]–[14], convex programming (CP) [15], [16], model predictive control (MPC) [17], and game theoretic approaches [18], [19]. An optimal centralized scheduling method to jointly control home appliances and PEVs is constructed as a mixed integer linear program (MILP) in [11]. A globally optimal and locally optimal scheduling scheme for EV charging and discharging are proposed using convex optimization in [16]. Energy management system for smart grids with PEVs based on hierarchical model predictive control (HiMPC) is presented in [17]. The problem of grid-to-vehicle energy exchange between a smart grid and plug-in electric vehicle groups (PEVGs) is studied using a noncooperative Stackelberg game in [18]. A strategic charging method for plugged in hybrid electric vehicles (PHEVs) in smart grids are introduced based Fig. 1. Structure of PEV to smart home. on a game theoretic approach in [19]. Rayati et al. [20] present a smart energy hub for a residential customer and use rein- D. Outline forcement learning and Monte Carlo estimation to find a near optimal solution for the energy management problem. These The remainder of the paper is arranged as follows. Section approaches all share a common goal, namely, to meet overall II details the models of PEV to smart home and the random home electric power demand while optimizing a metric such variables. The SDP framework is described in Section III. The as electricity consumption, reliability, or frequency regulation. optimization results are discussed in Section IV followed by Most of this literature pursues a V2G technology potential conclusions presented in Section V. evaluation objective. Few pursue a real-time control system II. PEV TO SMART HOME MODEL DEVELOPMENT that optimizes energy management with an explicit consider- ation for stochastic home loads and mobility patterns. Iverson A. Operating Modes et al. consider probabilities of vehicle departure time and We consider a smart home with PEV energy storage as trip duration to formulate a stochastic dynamic programming shown in Fig. 1. The smart home is composed of the house (SDP) algorithm to optimally charge an EV based on an load demand, the utility grid, a PEV with a Li-ion battery pack, inhomogeneous Markov chain model [21]. Donadee et al. [22] and associated power electronics. The PEV battery interfaces use stochastic models of (i) plug-in and plug-out behavior, with the utility grid and house loads via power electronics, (ii) energy required for transportation, and (iii) electric en- namely a DC/AC converter. The power electronics are de- ergy prices. These stochastic models are incorporated into an signed and controlled to allow bidirectional or unidirectional infinite-horizon Markov decision process (MDP) to minimize power flow according to the different operating modes. The the sum of electric energy charging costs, driving costs, and controller is used to manage the power flow between the the cost of any driver inconvenience. A later study by [14] con- battery, house appliances and utility grid. We apply a stochastic structs a Markov chain to model random prices and regulation optimal control approach to synthesize an energy management signal and formulates a SDP to optimize the charging and fre- controller. quency regulation capacity bids of an EV. The previous three There are four operating modes in PEV to smart home studies, however, do not consider integrated PEV charging systems, as shown in Fig. 2. Mode A allows PEV battery with building loads. Liang et al. [23] provide a comprehensive charging only (uni-directional power flow), called grid-to- literature survey on stochastic modeling and optimization tools vehicle (G2V). Mode B allows battery charge and discharge for microgrids and examine the effectiveness of such tools. with the home, but does not export power to the grid. This is called vehicle-to-home (V2H). Mode C allows battery charge C. Main Contribution and discharge and may sell power to the grid, called vehicle- The main contribution of this paper is to model PEV to-grid (V2G). The V2G mode with renewable energy (such energy storage availability by incorporating multiple random as solar rooftop photovoltaics or wind power) is considered variables into a SDP control formulation of smart home energy in mode D. Renewable energy sources will not be considered management. The random variables include PEV arrival time, in this paper, however it is a direct extension of the proposed departure time, and energy required for mobility. We also framework and a topic for future investigation. quantify the potential cost savings of various operating modes, To develop and evaluate our control methods, we consider including V2G, V2H, and G2V. Our procedure provides a load data from a single family home in Los Angeles, Califor- systematic methodology to quantify the potential cost savings nia. The collected data corresponds to date range 2013-04-01 to home ratepayers in smart homes with PEV energy storage. to 2014-03-31. Figure 3 plots the hourly average electricity Household Household Consequently, we can compute the power of the PEV battery A) Electricity Demand B) Electricity Demand as 2 Pbatt;k = Voc(SOCk)Ik + RintI (5) PEV Utility PEV Utility k Battery Grid Battery Grid The charge power is assumed to be positive, by convention.